README for "Replication Data for: False Equivalencies: Online activism from left to right"

Author: Deen Freelon, Ph.D. (freelon@email.unc.edu)

URL: https://doi.org/10.7910/DVN/ZH1EWA

This file contains basic instructions for how to replicate the underlying data found in the original empirical analyses in the following article:

Freelon, D., Marwick, A., & Kreiss, D. (forthcoming). False Equivalencies: Online activism from left to right. Science.

Some code adjustments may be required depending on how the data are reconstituted. Results will likely differ from those in the article due to data attrition.

1. Replicating the data underlying Figure 1

Required files: generate_blm_date_counts.py, blm_ids.xip

Instructions: First, make sure to change the file extension for blm_ids.xip to ".zip," then hydrate the Twitter data in the text files within. This may be done with a program such as Twarc: https://github.com/DocNow/twarc . Generate a text file consisting of a single column containing the 'created_at' metadata field which contains the tweet posting date, one date per line. The dates need to be in ISO (yyyy-mm-dd) format, e.g. "2014-06-01." Now execute generate_blm_date_counts.py, making sure your file names and column names match those in the script.

2. Replicating the data underlying Figure 2

Required files: partition_count.py, false_equiv_fig2_twitter_ids.xip, netp_twitter.csv, election_retweeter_polarization_media_scores.csv (not included)

Instructions: First, make sure to change the file extension for false_equiv_fig2_twitter_ids.xip to ".zip," then hydrate the Twitter data in the text file within. This may be done with a program such as Twarc: https://github.com/DocNow/twarc . Generate a CSV file containing two columns: the 'screen_name' or equivalent field in the first column, and the 'tweet_text' or equivalent field in the second. Download the election_retweeter_polarization_media_scores.csv file from https://doi.org/10.7910/DVN/0YDIBD/DCS31J . Now execute partition_count.py, making sure your file names and column names match those in the script. Be aware that this script may take up to 12 hours to complete.

3. Replicating the data underlying the "plandemic" and "anonymous trump" analyses

Required files: generate_top_rted.py, anon_trump_ids.txt, plandemic_ids.txt

Instructions: Hydrate the Twitter data in the anon_trump_ids.txt and plandemic_ids.txt files. This may be done with a program such as Twarc: https://github.com/DocNow/twarc . Generate a CSV file containing two columns: the 'screen_name' or equivalent field in the first column, and the 'retweet_count' or equivalent field in the second. Now execute generate_top_rted.py, making sure your file names and column names match those in the script.